Evidence map›Paper›PMID 41585140›Full record

ReviewJournal of dental sciences2026

Artificial intelligence applications in the diagnosis and management of cleft lip and palate: An updated review.

Tu Manh Nguyen, Uyen Ngoc Thao Huynh, Thi Thuy Tien Vo, Yang-Che Wu, Chien-Fu Tseng, I-Ta Lee

Abstract readReview
In one paragraph

Review in Journal of dental sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Tu Manh NguyenFaculty of Dentistry, Nguyen Tat Thanh University, Ho Chi Minh City, Viet Nam.
Uyen Ngoc Thao HuynhFaculty of Dentistry, Nguyen Tat Thanh University, Ho Chi Minh City, Viet Nam.
Thi Thuy Tien VoFaculty of Dentistry, Nguyen Tat Thanh University, Ho Chi Minh City, Viet Nam.
Yang-Che WuSchool of Dentistry, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan.
Chien-Fu TsengGraduate Institute of Clinical Dentistry, School of Dentistry, College of Medicine, National Taiwan University, Taipei, Taiwan.
I-Ta LeeSchool of Dentistry, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

According to the U.S. National Institutes of Health, cleft lip and/or palate (CL/P) is one of the most common congenital anomalies, significantly affecting both function and aesthetics while placing a considerable burden on healthcare systems worldwide. With the rapid advancement of artificial intelligence (AI) in various medical fields, a thorough evaluation of its role in CL/P management has become essential. Therefore, this review was undertaken to summarize recent clinical applications of AI in the diagnosis, treatment, and care of CL/P. A comprehensive search of PubMed and IEEE Xplore was conducted from January 1, 2015, to May 31, 2025, using combined keywords related to AI and CL/P. Of the 134 records initially identified, 51 full-text articles met the eligibility criteria and were included in the final analysis. In conclusion, AI is driving innovation in CL/P management across multiple domains; however, further evidence from diverse populations and the establishment of clear ethical frameworks are required to ensure its long-term clinical applicability.

Indexed as

Artificial intelligenceCleft lip and/or palateCraniofacial anomaliesDeep learningMachine learning

Identifiers

PMID41585140
PMCPMC12826031

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.